[Chore]:Extract math and argparse utilities to separate modules (#27188)

Signed-off-by: Yeshwanth Surya <yeshsurya@gmail.com>
Signed-off-by: Yeshwanth N <yeshsurya@gmail.com>
Signed-off-by: yeshsurya <yeshsurya@gmail.com>

benchmarks/benchmark_block_pool.py

@@ -5,7 +5,7 @@ import gc
 from benchmark_utils import TimeCollector
 from tabulate import tabulate
 
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.v1.core.block_pool import BlockPool
 
 

benchmarks/benchmark_long_document_qa_throughput.py

@@ -46,7 +46,7 @@ import time
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 
 def test_long_document_qa(llm=None, sampling_params=None, prompts=None):

benchmarks/benchmark_ngram_proposer.py

@@ -19,7 +19,7 @@ from vllm.config import (
     VllmConfig,
 )
 from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
 from vllm.v1.worker.gpu_input_batch import InputBatch
 from vllm.v1.worker.gpu_model_runner import GPUModelRunner

benchmarks/benchmark_prefix_caching.py

@@ -37,7 +37,7 @@ from transformers import PreTrainedTokenizerBase
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 try:
     from vllm.transformers_utils.tokenizer import get_tokenizer

benchmarks/benchmark_prioritization.py

@@ -11,7 +11,7 @@ import time
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 
 # Select a equi-probable random priority

benchmarks/benchmark_serving_structured_output.py

@@ -51,7 +51,7 @@ except ImportError:
     from backend_request_func import get_tokenizer
 
 try:
-    from vllm.utils import FlexibleArgumentParser
+    from vllm.utils.argparse_utils import FlexibleArgumentParser
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -15,7 +15,7 @@ from utils import make_rand_sparse_tensors
 from weight_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -18,7 +18,8 @@ from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     w8a8_triton_block_scaled_mm,
 )
-from vllm.utils import FlexibleArgumentParser, cdiv
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.math_utils import cdiv
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -10,7 +10,7 @@ import torch
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.triton_utils import triton
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 

benchmarks/kernels/benchmark_activation.py

@@ -10,7 +10,7 @@ import vllm.model_executor.layers.activation  # noqa F401
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 batch_size_range = [1, 16, 32, 64, 128]

benchmarks/kernels/benchmark_bitblas.py

@@ -28,7 +28,7 @@ except ImportError as e:
 
 from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target
 
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 parser = FlexibleArgumentParser(
     description="Benchmark BitBLAS int4 on a specific target."

benchmarks/kernels/benchmark_cutlass_fp4_moe.py

@@ -20,7 +20,7 @@ from vllm.model_executor.layers.fused_moe.config import (
 from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.scalar_type import scalar_types
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 WEIGHT_SHAPES_MOE = {
     "nvidia/DeepSeek-R1-FP4": [

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -14,7 +14,7 @@ from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_confi
 from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 # Weight shapes for different models: [num_experts, topk, hidden_size,
 # intermediate_size]

benchmarks/kernels/benchmark_device_communicators.py

@@ -39,7 +39,7 @@ from vllm.distributed.device_communicators.pynccl_allocator import (
 )
 from vllm.distributed.device_communicators.symm_mem import SymmMemCommunicator
 from vllm.logger import init_logger
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 logger = init_logger(__name__)
 

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -13,7 +13,7 @@ from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_experts,
     fused_topk,
 )
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = [
     "nm-testing/Mixtral-8x7B-Instruct-v0.1",

benchmarks/kernels/benchmark_layernorm.py

@@ -7,7 +7,7 @@ import torch
 
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 

benchmarks/kernels/benchmark_lora.py

@@ -25,7 +25,7 @@ if HAS_TRITON:
     from vllm.lora.ops.triton_ops import LoRAKernelMeta, lora_expand, lora_shrink
     from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
 
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_TP_SIZES = [1]

benchmarks/kernels/benchmark_machete.py

@@ -33,7 +33,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     quantize_weights,
 )
 from vllm.scalar_type import ScalarType, scalar_types
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-3-8b", "meta-llama/Llama-2-70b-hf"]
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512, 1024]

benchmarks/kernels/benchmark_marlin.py

@@ -44,7 +44,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     sort_weights,
 )
 from vllm.scalar_type import ScalarType, scalar_types
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]

benchmarks/kernels/benchmark_moe.py

@@ -22,7 +22,7 @@ from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
 from vllm.triton_utils import triton
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()